Overview

We manually curated 500\(+\) multiple modality PPMI MRI subjects. 5 messages from this effort.

Raw data quality is overall high

visual inspection by humans

Processing improves quality/usability further

failure rates humans vs processed
we provide scientists handles on this so that can be “strict” or “loose” in data selection
we document these parameters

Detailed curation results provide a unique resource in itself

for algorithm developers
- we ourselves benefit from this (will show examples)
for scientists
- to have confidence (or at least context regarding quality) in looking at automated IDPs derived from these data

Enables investigation of the “meaning” of M3RI at the individual level
Empowers investigation of the “meaning” of M3RI at the population level

the PPMI500 cohort

Reviewing multiple modality data quality for current phase of collection
we collaborated on cohort definition
- first phase - select subjects based on sampling from sites
- second phase - select subjects based on population characteristics (MJFF/PPMI)
show table of summary cohort

Table 1. Baseline cohort for subjects with T1w IDPs and non-missing diagnosis.
	CN	GenAR	LRRK2PD	SporadicAR	SporadicPD	p
	(N=47)	(N=1)	(N=4)	(N=230)	(N=217)	p
age	63.5 ± 12.5	71.9 ± NA	59.3 ± 7.1	67.7 ± 5.7	64.5 ± 9.4	< 0.001
Sex						0.026
Female	23 (48.9%)	1 (100.0%)	3 (75.0%)	102 (44.3%)	72 (33.2%)
Male	24 (51.1%)	0 ( 0.0%)	1 (25.0%)	128 (55.7%)	145 (66.8%)
duration.yrs			2.1 ± 0.7		1.0 ± 0.7	0.002
updrs.totscore	5.3 ± 3.8	14.0 ± NA	30.3 ± 13.1	12.1 ± 9.3	36.5 ± 15.0	< 0.001
CSFSAA						< 0.001
Negative	36 (85.7%)	1 (100.0%)	2 (50.0%)	82 (38.7%)	10 ( 4.9%)
Positive	6 (14.3%)	0 ( 0.0%)	2 (50.0%)	130 (61.3%)	194 (95.1%)
LEDD	0.0 ± 0.0	0.0 ± NA	259.0 ± 317.7	1.3 ± 19.8	3.6 ± 30.3	< 0.001
hasDTI						0.117
FALSE	9 (19.1%)	1 (100.0%)	1 (25.0%)	57 (24.8%)	38 (17.5%)
TRUE	38 (80.9%)	0 ( 0.0%)	3 (75.0%)	173 (75.2%)	179 (82.5%)
hasfMRI						0.003
FALSE	5 (10.6%)	1 (100.0%)	0 ( 0.0%)	12 ( 5.2%)	15 ( 6.9%)
TRUE	42 (89.4%)	0 ( 0.0%)	4 (100.0%)	218 (94.8%)	202 (93.1%)

1. Data quality is overall high

How do we define M3RI quality?

we manually QC’d each modality for each of 500 baseline subjects
- characterize reasons for failure

what is the QC protocol?

Review QC protocol – generally speaking

what are the QC results?

Summarize QC results for each modality
- raw data QC and failure rates
Summarize results for “joint” quality - how many subjects are high quality across all modalities at once?

Tables of Manual QC results

Table 2 Patterns of QC results by modality: FLAIR
	T2Flair	p
	(N=549)	p
qcfail_orientation
True	1 (100.0%)
qcfail_intensity_saturation
True	4 (100.0%)
qcfail_background_noise
True	6 (100.0%)
qcfail_motion
True	10 (100.0%)
qcfail_other
True	2 (100.0%)
qchuman_FLAIR		< 0.001
False	25 ( 4.6%)
True	524 (95.4%)
has_humanqc
1	549 (100.0%)

Table 3 Patterns of QC results by modality: T1w
	T1w	p
	(N=523)	p
qcfail_background_noise
True	2 (100.0%)
qcfail_motion
True	4 (100.0%)
qcfail_other
True	4 (100.0%)
qchuman_T1w		< 0.001
False	10 ( 1.9%)
True	513 (98.1%)
has_humanqc
1	523 (100.0%)

Table 4 Patterns of QC results by modality: NM
	Neuromelanin	p
	(N=502)	p
NM_QC_Ratings_Z		< 0.001
0	88 (18.3%)
1	393 (81.7%)
NM_QC_Ratings_SNVol		< 0.001
0	7 ( 1.5%)
1	474 (98.5%)
NM_QC_Ratings_AvgIntensity		< 0.001
0	5 ( 1.0%)
1	473 (99.0%)
NM_QC_Ratings_SDIntensity		< 0.001
0	5 ( 1.0%)
1	473 (99.0%)
NM_QC_Ratings_MaxIntensity		< 0.001
0	5 ( 1.0%)
1	473 (99.0%)
NM_QC_Ratings_Failures		< 0.001
0	386 (80.8%)
1	81 (16.9%)
2	6 ( 1.3%)
3	4 ( 0.8%)
4	1 ( 0.2%)

Table 5 Patterns of QC results by modality: DTI
	DTIb0	DTIdwi	p
	(N=1204)	(N=1340)	p
qcfail_orientation
True	16 (100.0%)	16 (100.0%)
qcfail_phantom
True	6 (100.0%)	6 (100.0%)
qcfail_intensity_saturation
True	0 ( 0.0%)	4 (100.0%)
qcfail_background_noise
True	50 (100.0%)	54 (100.0%)
qcfail_motion
True	54 (100.0%)	70 (100.0%)
qcfail_signal_dropout
True	14 (100.0%)	22 (100.0%)
qcfail_other			1.000
GHOSTING	10 (62.5%)	10 (62.5%)
True	6 (37.5%)	6 (37.5%)
qchuman_DTI			0.481
False	94 ( 7.8%)	116 ( 8.7%)
True	1110 (92.2%)	1224 (91.3%)
has_humanqc
1	1204 (100.0%)	1340 (100.0%)

Table 6 Patterns of QC results by modality: rsfMRI
	rsfMRI	rsfMRI_LR	rsfMRI_RL	p
	(N=2969)	(N=187)	(N=854)	p
qcfail_orientation
True	9 (100.0%)	1 (100.0%)	16 (100.0%)
qcfail_phantom
True	14 (100.0%)	0 ( 0.0%)	0 ( 0.0%)
qcfail_spacing
True	14 (100.0%)	0 ( 0.0%)	0 ( 0.0%)
qcfail_intensity_saturation
True	20 (100.0%)	10 (100.0%)	32 (100.0%)
qcfail_motion
True	581 (100.0%)	0 ( 0.0%)	0 ( 0.0%)
qcfail_signal_dropout
True	165 (100.0%)	15 (100.0%)	48 (100.0%)
qcfail_temporal_noise
True	1 (100.0%)	1 (100.0%)	8 (100.0%)
qcfail_insufficient_volumes
True	12 (100.0%)	0 ( 0.0%)	0 ( 0.0%)
qcfail_FOV
True	1 (100.0%)	0 ( 0.0%)	0 ( 0.0%)
qcfail_other				< 0.001
472 volumes, restart appended to initial scan?	0 ( 0.0%)	0 ( 0.0%)	8 (33.3%)
axial slices look wrong way around?	0 ( 0.0%)	1 (50.0%)	8 (33.3%)
strange artifacts in tsnr	0 ( 0.0%)	1 (50.0%)	8 (33.3%)
True	1 (50.0%)	0 ( 0.0%)	0 ( 0.0%)
wrap-around artifact	1 (50.0%)	0 ( 0.0%)	0 ( 0.0%)
qchuman_rsfMRI				0.002
False	457 (15.4%)	19 (10.2%)	96 (11.2%)
True	2512 (84.6%)	168 (89.8%)	758 (88.8%)
has_humanqc
1	2969 (100.0%)	187 (100.0%)	854 (100.0%)

T1w results

T1w QC summary
modality	Total	Successes	Failures	Percent_Success	Percent_Failure	failcat
T1w	523	513	10	98.08795	1.9120459	qchuman_T1w
T1w	523	521	2	99.61759	0.3824092	qcfail_background_noise
T1w	523	519	4	99.23518	0.7648184	qcfail_motion
T1w	523	519	4	99.23518	0.7648184	qcfail_other

Flair results

FLAIR QC summary
modality	Total	Successes	Failures	Percent_Success	Percent_Failure	failcat
T2Flair	549	524	25	95.44627	4.5537341	qchuman_FLAIR
T2Flair	549	548	1	99.81785	0.1821494	qcfail_orientation
T2Flair	549	545	4	99.27140	0.7285974	qcfail_intensity_saturation
T2Flair	549	543	6	98.90710	1.0928962	qcfail_background_noise
T2Flair	549	539	10	98.17851	1.8214936	qcfail_motion
T2Flair	549	547	2	99.63570	0.3642987	qcfail_other

DT results

DTI QC summary
modality	Total	Successes	Failures	Percent_Success	Percent_Failure	failcat
DTIb0	1204	1110	94	92.19269	7.8073090	qchuman_DTI
DTIdwi	1340	1224	116	91.34328	8.6567164	qchuman_DTI
DTIb0	1204	1188	16	98.67110	1.3289037	qcfail_orientation
DTIdwi	1340	1324	16	98.80597	1.1940299	qcfail_orientation
DTIb0	1204	1198	6	99.50166	0.4983389	qcfail_phantom
DTIdwi	1340	1334	6	99.55224	0.4477612	qcfail_phantom
DTIb0	1204	1204	0	100.00000	0.0000000	qcfail_intensity_saturation
DTIdwi	1340	1336	4	99.70149	0.2985075	qcfail_intensity_saturation
DTIb0	1204	1154	50	95.84718	4.1528239	qcfail_background_noise
DTIdwi	1340	1286	54	95.97015	4.0298507	qcfail_background_noise
DTIb0	1204	1150	54	95.51495	4.4850498	qcfail_motion
DTIdwi	1340	1270	70	94.77612	5.2238806	qcfail_motion
DTIb0	1204	1190	14	98.83721	1.1627907	qcfail_signal_dropout
DTIdwi	1340	1318	22	98.35821	1.6417910	qcfail_signal_dropout
DTIb0	1204	1188	16	98.67110	1.3289037	qcfail_other
DTIdwi	1340	1324	16	98.80597	1.1940299	qcfail_other

rsfMRI results

rsfMRI QC summary
modality	Total	Successes	Failures	Percent_Success	Percent_Failure	failcat
rsfMRI	2969	2512	457	84.60761	15.3923880	qchuman_rsfMRI
rsfMRI_LR	187	168	19	89.83957	10.1604278	qchuman_rsfMRI
rsfMRI_RL	854	758	96	88.75878	11.2412178	qchuman_rsfMRI
rsfMRI	2969	2960	9	99.69687	0.3031324	qcfail_orientation
rsfMRI_LR	187	186	1	99.46524	0.5347594	qcfail_orientation
rsfMRI_RL	854	838	16	98.12646	1.8735363	qcfail_orientation
rsfMRI	2969	2955	14	99.52846	0.4715392	qcfail_phantom
rsfMRI_LR	187	187	0	100.00000	0.0000000	qcfail_phantom
rsfMRI_RL	854	854	0	100.00000	0.0000000	qcfail_phantom
rsfMRI	2969	2955	14	99.52846	0.4715392	qcfail_spacing
rsfMRI_LR	187	187	0	100.00000	0.0000000	qcfail_spacing
rsfMRI_RL	854	854	0	100.00000	0.0000000	qcfail_spacing
rsfMRI	2969	2949	20	99.32637	0.6736275	qcfail_intensity_saturation
rsfMRI_LR	187	177	10	94.65241	5.3475936	qcfail_intensity_saturation
rsfMRI_RL	854	822	32	96.25293	3.7470726	qcfail_intensity_saturation
rsfMRI	2969	2388	581	80.43112	19.5688784	qcfail_motion
rsfMRI_LR	187	187	0	100.00000	0.0000000	qcfail_motion
rsfMRI_RL	854	854	0	100.00000	0.0000000	qcfail_motion
rsfMRI	2969	2804	165	94.44257	5.5574267	qcfail_signal_dropout
rsfMRI_LR	187	172	15	91.97861	8.0213904	qcfail_signal_dropout
rsfMRI_RL	854	806	48	94.37939	5.6206089	qcfail_signal_dropout
rsfMRI	2969	2968	1	99.96632	0.0336814	qcfail_temporal_noise
rsfMRI_LR	187	186	1	99.46524	0.5347594	qcfail_temporal_noise
rsfMRI_RL	854	846	8	99.06323	0.9367681	qcfail_temporal_noise
rsfMRI	2969	2957	12	99.59582	0.4041765	qcfail_insufficient_volumes
rsfMRI_LR	187	187	0	100.00000	0.0000000	qcfail_insufficient_volumes
rsfMRI_RL	854	854	0	100.00000	0.0000000	qcfail_insufficient_volumes
rsfMRI	2969	2968	1	99.96632	0.0336814	qcfail_FOV
rsfMRI_LR	187	187	0	100.00000	0.0000000	qcfail_FOV
rsfMRI_RL	854	854	0	100.00000	0.0000000	qcfail_FOV

NM results

NM QC summary
NM.QC.cat	Total	Successes	Failures	Percent_Success	Percent_Failure	failcat
overall	478	386	92	80.75314	19.246862	NM_QC_Ratings_Failures
z.coord	478	391	87	81.79916	18.200837	NM_QC_Ratings_Z
sn.vol	478	471	7	98.53556	1.464435	NM_QC_Ratings_SNVol
avg.int	478	473	5	98.95397	1.046025	NM_QC_Ratings_AvgIntensity
sd.int	478	473	5	98.95397	1.046025	NM_QC_Ratings_SDIntensity
max.int	478	473	5	98.95397	1.046025	NM_QC_Ratings_MaxIntensity

do a check based on rsfmri motion – if the qc data “makes sense”

do a check based on DTI – if the qc data “makes sense”

do a check based on NM – if the qc data “makes sense”

do a check based on FLAIR – if the qc data “makes sense”

do a check based on T1w – if the qc data “makes sense”

2. Detailed curation results provide a unique resource in itself

for algorithm developers

we ourselves benefit from this (will show examples)

for scientists

to have confidence (or at least context regarding quality) in looking at automated IDPs derived from these data

3. Processing improves quality/usability further

effect of processing by modality

for each, summarize how processing improves the data
- rsfMRI is “easy” to see - throw away motion corrupted data
- developed methodology specific for PPMI that was informed by this process
- automated QC for these modalities for future work

we provide scientists handles on this so that can be “strict” or “loose” in data selection

we document these parameters

4. Enables investigation of the “meaning” of M3RI at the individual level

5. Empowers investigation of the “meaning” of M3RI at the population level

simlr

## [1] "     <0> BUILD-V <0> BUILD-V <0> BUILD-V <0> BUILD-V <0>    "
## [1] "initialDataTerm: -0.372650010297621  <o> mixer: pca  <o> E:  cca"
## [1] "Iteration: 1 bestEv: -0.0327933507588981 bestIt: 1 CE: -0.0327933507588981"
## [1] "Iteration: 2 bestEv: -0.0630145184881478 bestIt: 2 CE: -0.0630145184881478"
## [1] "Iteration: 3 bestEv: -0.0968209008706581 bestIt: 3 CE: -0.0968209008706581"
## [1] "Iteration: 4 bestEv: -0.0968209008706581 bestIt: 3 CE: -0.0870532158412072"
## [1] "Iteration: 5 bestEv: -0.0968209008706581 bestIt: 3 CE: -0.0869174491918662"
## [1] "Iteration: 6 bestEv: -0.0968209008706581 bestIt: 3 CE: -0.092816820015101"
## [1] "Iteration: 7 bestEv: -0.120174060553191 bestIt: 7 CE: -0.120174060553191"
## [1] "Iteration: 8 bestEv: -0.120174060553191 bestIt: 7 CE: -0.120174060553191"
## [1] "Iteration: 9 bestEv: -0.12581493976007 bestIt: 9 CE: -0.12581493976007"
## [1] "Iteration: 10 bestEv: -0.128566404189215 bestIt: 10 CE: -0.128566404189215"
## [1] "Iteration: 11 bestEv: -0.129545856667216 bestIt: 11 CE: -0.129545856667216"
## [1] "Iteration: 12 bestEv: -0.13209953844886 bestIt: 12 CE: -0.13209953844886"
## [1] "Iteration: 13 bestEv: -0.136764239989398 bestIt: 13 CE: -0.136764239989398"
## [1] "Iteration: 14 bestEv: -0.136764239989398 bestIt: 13 CE: -0.0756392704157992"
## [1] "Iteration: 15 bestEv: -0.136764239989398 bestIt: 13 CE: -0.0801460646909918"
## [1] "Iteration: 16 bestEv: -0.136764239989398 bestIt: 13 CE: -0.0813127215892158"
## [1] "Iteration: 17 bestEv: -0.136764239989398 bestIt: 13 CE: -0.0813127215892158"
## [1] "Iteration: 18 bestEv: -0.136764239989398 bestIt: 13 CE: -0.136546894715855"
## [1] "Iteration: 19 bestEv: -0.139231177436898 bestIt: 19 CE: -0.139231177436898"
## [1] "Iteration: 20 bestEv: -0.147375833711929 bestIt: 20 CE: -0.147375833711929"
## [1] "Iteration: 21 bestEv: -0.147375833711929 bestIt: 20 CE: -0.140165912343255"
## [1] "Iteration: 22 bestEv: -0.147375833711929 bestIt: 20 CE: -0.140165912343255"
## [1] "Iteration: 23 bestEv: -0.147375833711929 bestIt: 20 CE: -0.140165912343255"
## [1] "Iteration: 24 bestEv: -0.147375833711929 bestIt: 20 CE: -0.140165912343255"
## [1] "Iteration: 25 bestEv: -0.147375833711929 bestIt: 20 CE: -0.142603300356657"
## [1] "Iteration: 26 bestEv: -0.147375833711929 bestIt: 20 CE: -0.142603300356657"
## [1] 0.0630929
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## [1] 0.0496055
## [1] "1 0.160545179779455"
## [1] 0.02284878
## [1] 0.08965439
## [1] 0.03945563
## [1] "1 0.0541901103364408"
## [1] 0.02106304
## [1] 0.06575828
## [1] 0.005659268
## [1] "1 0.0587620768638725"
## [1] 0.01570877
## [1] 0.07027293
## [1] 0.05540485
## [1] "1 0.0853013080067246"
## [1] 0.02165442
## [1] 0.06689294
## [1] 0.05584299
## [1] "1 0.00559602156321104"
## [1] 0.04891857
## [1] 0.08293585
## [1] 0.02674207
## [1] "1 0.00602111137916511"
## [1] 0.02753027
## [1] 0.08932279
## [1] 0.03674929
## [1] "1 0.0913966257147703"
## [1] 0.03140498
## [1] 0.04357756
## [1] 0.03737233
## [1] "1 0.350222593167011"
## [1] 0.03241135
## [1] 0.0401226
## [1] 0.06252411
## [1] "1 0.108368520216622"
## [1] 0.03677997
## [1] 0.1317392
## [1] 0.009142108
## [1] "1 0.801614903606815"
## [1] 0.0459863
## [1] 0.06341489
## [1] 0.03998678
## [1] "1 0.00438758200079222"
## [1] 0.06326474
## [1] 0.0289275
## [1] 0.0334835
## [1] "1 0.0620506443334716"
## [1] 0.04155094
## [1] 0.03922288
## [1] 0.02816804
## [1] "1 0.0929356483918441"
## [1] 0.04992943
## [1] 0.1033501
## [1] 0.03095999
## [1] "1 0.0400357755810751"
## [1] 0.04161823
## [1] 0.05429088
## [1] 0.05313681
## [1] "1 0.0724555855092227"
## [1] 0.04052251
## [1] 0.08370206
## [1] 0.02893663
## [1] "1 0.402128051312556"
## [1] 0.03919301
## [1] 0.03660509
## [1] 0.03844216
## [1] "1 0.0569052157038563"
## [1] 0.01436859
## [1] 0.01814266
## [1] 0.04979079
## [1] "1 0.0750359938885412"
## [1] 0.04493444
## [1] 0.1125809
## [1] 0.07546835
## [1] "1 0.0549330571836182"
## [1] 0.02603415
## [1] 0.05414639
## [1] 0.06647391
## [1] "1 0.118521595218043"
## [1] 0.04272542
## [1] 0.07975711
## [1] 0.04239525
## [1] "1 0.4452017304738"
## [1] 0.04304212
## [1] 0.05476584
## [1] 0.03708388
## [1] "1 0.00330776124436703"
## [1] 0.03181756
## [1] 0.03243388
## [1] 0.04640582
## [1] "1 0.948508253740791"
## [1] 0.06655578
## [1] 0.06839427
## [1] 0.01590732
## [1] "1 0.72330040215693"
## [1] 0.0154856
## [1] 0.02257197
## [1] 0.02519842
## [1] "1 0.071921885311281"
## [1] 0.01837715
## [1] 0.05024716
## [1] 0.04997093
## [1] "1 0.617639396084375"
## [1] 0.0605691
## [1] 0.116757
## [1] 0.07546761
## [1] "1 0.268881609018105"
## [1] 0.06633092
## [1] 0.08038766
## [1] 0.08946624
## [1] "1 0.0635555173143578"
## [1] 0.06078885
## [1] 0.07235237
## [1] 0.03320166
## [1] "1 0.0772495056408294"
## [1] 0.06277849
## [1] 0.06907499
## [1] 0.02451351
## [1] "1 0.652967102154168"
## [1] 0.03819196
## [1] 0.08308102
## [1] 0.05618294
## [1] "1 0.102773856216944"
## [1] 0.02484044
## [1] 0.07820951
## [1] 0.008306815
## [1] "1 0.0832899537153189"
## [1] 0.03610768
## [1] 0.06570733
## [1] 0.04691317
## [1] "1 0.051203573311852"
## [1] 0.04241024
## [1] 0.07551714
## [1] 0.02287316
## [1] "1 0.0126698285088394"
## [1] 0.03192555
## [1] 0.04344844
## [1] 0.03156755
## [1] "1 0.00956121475379167"
## [1] 0.02104856
## [1] 0.0649079
## [1] 0.0180208
## [1] "1 0.16148777507621"
## [1] 0.01960253
## [1] 0.04306293
## [1] 0.04180044
## [1] "1 0.0503950715107181"
## [1] 0.04204938
## [1] 0.06589457
## [1] 0.02142847
## [1] "1 0.183010415225421"
## [1] 0.04359833
## [1] 0.06302444
## [1] 0.03822532
## [1] "1 0.174111623702899"
## [1] 0.04065948
## [1] 0.05185999
## [1] 0.0346206
## [1] "1 0.0199213036312554"
## [1] 0.03220299
## [1] 0.06735252
## [1] 0.04200663
## [1] "1 0.274081720575057"
## [1] 0.01462172
## [1] 0.03904959
## [1] 0.01407211
## [1] "1 0.0467825633887689"
## [1] 0.06881481
## [1] 0.08099738
## [1] 0.04328546
## [1] "1 0.122582259172455"
## [1] 0.02522416
## [1] 0.04433409
## [1] 0.05660003
## [1] "1 0.590313197992923"
## [1] 0.04195973
## [1] 0.04705147
## [1] 0.01997471
## [1] "1 0.47715636518013"
## [1] 0.03639515
## [1] 0.04717104
## [1] 0.03548255
## [1] "1 0.0128221632649177"
## [1] 0.03828957
## [1] 0.0419498
## [1] 0.0197351
## [1] "1 0.0210430656699457"
## [1] 0.03973611
## [1] 0.06136413
## [1] 0.0256119
## [1] "1 0.249424783537397"
## [1] 0.04674051
## [1] 0.06373942
## [1] 0.01353346
## [1] "1 0.20309331060424"
## [1] 0.05356098
## [1] 0.09452549
## [1] 0.04410354
## [1] "1 0.56699252753519"
## [1] 0.02876336
## [1] 0.09104336
## [1] 0.02855228
## [1] "1 0.0442650152963898"
## [1] 0.04259282
## [1] 0.09626909
## [1] 0.0229427
## [1] "1 0.0254021432991888"
## [1] 0.03816867
## [1] 0.04962869
## [1] 0.03666615
## [1] "1 0.0330042481634965"
## [1] 0.005864365
## [1] 0.03390455
## [1] 0.02994949
## [1] "1 0.0415025307632421"
## [1] 0.03045308
## [1] 0.0453398
## [1] 0.03063926
## [1] "1 0.0485317867560451"
## [1] 0.02098169
## [1] 0.04667136
## [1] 0.0205433
## [1] "1 0.221197703317231"
## [1] 0.02825009
## [1] 0.08395947
## [1] 0.0163654
## [1] "1 0.0331833196804612"
## [1] 0.06827637
## [1] 0.08456064
## [1] 0.06209281
## [1] "1 0.113786966265866"
## [1] 0.03580802
## [1] 0.07874386
## [1] 0.01894777
## [1] "1 0.717608326962784"
## [1] 0.05758521
## [1] 0.04530091
## [1] 0.01051303
## [1] "1 0.18398831889247"
## [1] 0.02186671
## [1] 0.04610322
## [1] 0.0374948
## [1] "1 0.0463069996687485"
## [1] 0.04641669
## [1] 0.04972749
## [1] 0.02195146
## [1] "1 0.14269496939746"
## [1] 0.06868643
## [1] 0.06292221
## [1] 0.05070293
## [1] "1 0.0185477389632554"
## [1] 0.05564
## [1] 0.08068596
## [1] 0.05502952
## [1] "1 0.587176282924374"
## [1] 0.04066368
## [1] 0.03527615
## [1] 0.03610686
## [1] "1 0.00132705617918233"
## [1] 0.05889093
## [1] 0.05160893
## [1] 0.06224246
## [1] "1 0.0670994235322177"
## [1] 0.02493003
## [1] 0.07788846
## [1] 0.01221224
## [1] "1 0.0242276829624712"
## [1] 0.02786918
## [1] 0.06390757
## [1] 0.02708857
## [1] "1 0.087813684505013"
## [1] 0.03467518
## [1] 0.07589571
## [1] 0.06250838
## [1] "1 0.31586539187297"
## [1] 0.05978308
## [1] 0.06222272
## [1] 0.02904438
## [1] "1 0.313657587625811"
## [1] 0.0309592
## [1] 0.04773233
## [1] 0.0354711
## [1] "1 0.0136265568514024"
## [1] 0.04713433
## [1] 0.05349283
## [1] 0.04030734
## [1] "1 0.559946659467991"
## [1] 0.06111391
## [1] 0.05720246
## [1] 0.06445698
## [1] "1 0.0557059143677796"
## [1] 0.02169783
## [1] 0.09261248
## [1] 0.03790334
## [1] "1 0.161030241134552"
## [1] 0.05513028
## [1] 0.06287655
## [1] 0.02330846
## [1] "1 0.141613043712074"
## [1] 0.04852659
## [1] 0.06958726
## [1] 0.02339817
## [1] "1 0.387182361652895"
## [1] 0.05788892
## [1] 0.0843384
## [1] 0.0216129
## [1] "1 0.0374318376245641"
## [1] 0.03105342
## [1] 0.03727803
## [1] 0.05677604
## [1] "1 0.0382186072401221"
## [1] 0.02512108
## [1] 0.07223662
## [1] 0.03961764
## [1] "1 0.0343678488559571"
## [1] 0.01854065
## [1] 0.04150163
## [1] 0.03064332
## [1] "1 0.0366600712226003"
## [1] 0.047196
## [1] 0.06133113
## [1] 0.01940903
## [1] "1 0.556195431398713"
## [1] 0.05281333
## [1] 0.04062543
## [1] 0.03744118
## [1] "1 0.161613250646088"
## [1] 0.03855365
## [1] 0.02801116
## [1] 0.02297174
## [1] "1 0.192306688094241"
## [1] 0.03333551
## [1] 0.0550211
## [1] 0.04167407
## [1] "1 0.725865168670112"
## [1] 0.06487669
## [1] 0.05997242
## [1] 0.00824551
## [1] "1 0.0595112379510438"
## [1] 0.0182962
## [1] 0.03256486
## [1] 0.04489314
## [1] "1 0.121290868638493"
## [1] 0.03392788
## [1] 0.06008074
## [1] 0.06343774
## [1] "1 0.00545932036091891"
## [1] 0.06208573
## [1] 0.0688373
## [1] 0.05679632
## [1] "1 0.699649464625945"
## [1] 0.009284302
## [1] 0.04557386
## [1] 0.01069078
## [1] "1 0.00652772077655523"
## [1] 0.02575567
## [1] 0.07885447
## [1] 0.03236572
## [1] "1 0.173394371085031"
## [1] 0.03213179
## [1] 0.07631367
## [1] 0.02437731
## [1] "1 0.251820688673548"
## [1] 0.04118437
## [1] 0.02579297
## [1] 0.03588077
## [1] "1 0.163872144705226"
## [1] 0.0558201
## [1] 0.0521762
## [1] 0.028283
## [1] "1 0.356186558503165"
## [1] 0.0109955
## [1] 0.06303201
## [1] 0.02731521
## [1] "1 0.138329646229258"
## [1] 0.04120813
## [1] 0.0495756
## [1] 0.02899944
## [1] "1 0.0528586571631784"
## [1] 0.05082004
## [1] 0.07823377
## [1] 0.04619435
## [1] "1 0.0170658100592988"
## [1] 0.00858112
## [1] 0.004596902
## [1] 0.009437389
## [1] "1 0.100905524126079"
## [1] 0.05102408
## [1] 0.08837339
## [1] 0.0531868
## [1] "1 0.0171382945300328"
## [1] 0.03291919
## [1] 0.0553402
## [1] 0.02796184
## [1] "1 0.337177807747296"
## [1] 0.05459665
## [1] 0.03900464
## [1] 0.03572071
## [1] "1 0.153461346358315"
## [1] 0.04726743
## [1] 0.06830262
## [1] 0.03259438
## [1] "1 0.415804645433589"
## [1] 0.0177201
## [1] 0.06061726
## [1] 0.02973647
## [1] "1 0.16571572102884"
## [1] 0.04017365
## [1] 0.09883411
## [1] 0.04943956
## [1] "1 0.16844120146565"
## [1] 0.03433618
## [1] 0.07503754
## [1] 0.03671217
## [1] "1 0.404340637721021"
## [1] 0.0340711
## [1] 0.04898522
## [1] 0.04845004
## [1] "1 0.0736905253475063"
## [1] 0.03212837
## [1] 0.07287336
## [1] 0.06014556
## [1] "1 0.604708481761244"
## [1] 0.06430525
## [1] 0.07926983
## [1] 0.0247576
## [1] "1 0.144684532290054"
## [1] 0.04720213
## [1] 0.09778086
## [1] 0.04477515
## [1] "1 0.0822021566562165"
## [1] 0.01946235
## [1] 0.04567663
## [1] 0.05489625
## [1] "1 0.00977641272576385"
## [1] 0.04997235
## [1] 0.08113583
## [1] 0.04181768
## [1] "1 0.227776216761175"
## [1] 0.05166893
## [1] 0.06268007
## [1] 0.04021258
## [1] "1 0.050305054255458"
## [1] 0.04414744
## [1] 0.05509166
## [1] 0.01084672
## [1] "1 0.222739058256784"
## [1] 0.01237579
## [1] 0.005355435
## [1] 0.01219065
## [1] "1 0.1687876333614"
## [1] 0.03688722
## [1] 0.1335837
## [1] 0.02755578
## [1] "1 0.485784902815789"
## [1] 0.07897884
## [1] 0.03711085
## [1] 0.02465954
## [1] "1 0.0123230111060842"
## [1] 0.0448215
## [1] 0.07793249
## [1] 0.01497113
## [1] "1 0.00453776382408584"
## [1] 0.02108004
## [1] 0.07203351
## [1] 0.02664318
## [1] "1 0.360589701654667"
## [1] 0.03816867
## [1] 0.04962869
## [1] 0.03666615
## [1] "1 0.0330042481634965"
## [1] 0.06027731
## [1] 0.08355711
## [1] 0.05492718
## [1] "1 0.539366919625769"
## [1] 0.03788948
## [1] 0.02895126
## [1] 0.03721508
## [1] "1 0.00432227230294628"
## [1] 0.04841777
## [1] 0.03165477
## [1] 0.033069
## [1] "1 0.0746250184154798"
## [1] 0.0630172
## [1] 0.0517884
## [1] 0.05298815
## [1] "1 0.0829313235481923"
## [1] 0.03965592
## [1] 0.06163888
## [1] 0.03030191
## [1] "1 0.358820793829194"
## [1] 0.009545613
## [1] 0.07979472
## [1] 0.01954466
## [1] "1 0.0585191639721679"
## [1] 0.06868643
## [1] 0.06292221
## [1] 0.05070293
## [1] "1 0.0185477389632554"
## [1] 0.07132661
## [1] 0.07377478
## [1] 0.02631914
## [1] "1 0.527004824669165"
## [1] 0.03441058
## [1] 0.09505394
## [1] 0.01677284
## [1] "1 0.0488496378477023"
## [1] 0.01610217
## [1] 0.07306119
## [1] 0.02858364
## [1] "1 0.0157040997167592"
## [1] 0.06325705
## [1] 0.1084706
## [1] 0.04432835
## [1] "1 0.0194102902393112"
## [1] 0.04399229
## [1] 0.07010635
## [1] 0.03007796
## [1] "1 0.307944925682457"
## [1] 0.05415916
## [1] 0.04006834
## [1] 0.02738588
## [1] "1 0.105228511657066"
## [1] 0.04336709
## [1] 0.06706398
## [1] 0.03467856
## [1] "1 0.0278014286085451"
## [1] 0.04909176
## [1] 0.09420704
## [1] 0.02671771
## [1] "1 0.152552318255103"
## [1] 0.04756534
## [1] 0.05353695
## [1] 0.01422154
## [1] "1 0.0159567027794037"
## [1] 0.04715246
## [1] 0.05558576
## [1] 0.02785374
## [1] "1 0.0467562147285988"
## [1] 0.04532046
## [1] 0.1340682
## [1] 0.02261944
## [1] "1 0.0205892991899342"
## [1] 0.04184438
## [1] 0.02770159
## [1] 0.03000734
## [1] "1 0.254904107118623"
## [1] 0.02094793
## [1] 0.05395701
## [1] 0.02896039
## [1] "1 0.662727805310266"
## [1] 0.02677782
## [1] 0.04816317
## [1] 0.02323123
## [1] "1 0.0720307668928356"
## [1] 0.04428827
## [1] 0.04965497
## [1] 0.01669672
## [1] "1 0.0597032323785957"
## [1] 0.06593947
## [1] 0.08173966
## [1] 0.02927645
## [1] "1 0.0102231872300007"
## [1] 0.05834271
## [1] 0.08402735
## [1] 0.03180263
## [1] "1 0.107761533416821"
## [1] 0.03695462
## [1] 0.05395324
## [1] 0.03501445
## [1] "1 0.0284423480687222"
## [1] 0.02674508
## [1] 0.05297481
## [1] 0.04305638
## [1] "1 0.0152850993354577"
## [1] 0.06800031
## [1] 0.07924679
## [1] 0.0293476
## [1] "1 0.137354527720979"
## [1] 0.04909237
## [1] 0.03752832
## [1] 0.02931085
## [1] "1 0.0163764661782822"
## [1] 0.03837174
## [1] 0.09515757
## [1] 0.01964217
## [1] "1 0.16916548982543"
## [1] 0.03735237
## [1] 0.06716148
## [1] 0.03686288
## [1] "1 0.0123505264571607"
## [1] 0.05561619
## [1] 0.1035607
## [1] 0.0543239
## [1] "1 0.678139952645981"
## [1] 0.04208442
## [1] 0.05712025
## [1] 0.01417198
## [1] "1 0.83175904487243"
## [1] 0.08236342
## [1] 0.09283336
## [1] 0.03569142
## [1] "1 0.774343043103551"
## [1] 0.04271123
## [1] 0.06501948
## [1] 0.04764576
## [1] "1 0.118975711224574"
## [1] 0.05231901
## [1] 0.04154095
## [1] 0.0189346
## [1] "1 0.504226123155032"
## [1] 0.04351042
## [1] 0.07920088
## [1] 0.04696099
## [1] "1 0.0498942977024452"
## [1] 0.02987292
## [1] 0.05440064
## [1] 0.05568823
## [1] "1 0.0205641883157072"
## [1] 0.03518637
## [1] 0.06482327
## [1] 0.01457954
## [1] "1 0.31120838382253"
## [1] 0.03586346
## [1] 0.06525843
## [1] 0.04924828
## [1] "1 0.0848066713776339"
## [1] 0.05856011
## [1] 0.05774023
## [1] 0.04834239
## [1] "1 0.140705367548224"
## [1] 0.04093962
## [1] 0.1164789
## [1] 0.05582157
## [1] "1 0.00227655973751552"

SiMLR permutation summary
permstate	anv	pvalt1	pvalrs	pvaldt
original	0.0000047	0.0011691	0.0079420	0.0003504
perm	0.0694300	0.0802097	0.2667030	0.0798390
perm	0.1072594	0.8680470	0.3132921	0.0222560
perm	0.0243461	0.1413078	0.5740906	0.0056556
perm	0.0063436	0.1798453	0.0449181	0.0167461
perm	0.0710965	0.1128826	0.9834749	0.0242626
perm	0.0240338	0.7456317	0.0665245	0.0125568
perm	0.0213913	0.2119596	0.3868857	0.0027174
perm	0.0803078	0.0382954	0.6368228	0.0916118
perm	0.1414967	0.2344531	0.5574916	0.0469212
perm	0.1050222	0.2381957	0.0667943	0.0745796
perm	0.1907766	0.4492391	0.3834974	0.0571751
perm	0.6243029	0.8945569	0.3675913	0.3459025
perm	0.0008337	0.0580824	0.1650137	0.0005346
perm	0.0643833	0.1016979	0.4763794	0.0315559
perm	0.1460269	0.6870602	0.0925345	0.1338071
perm	0.0106873	0.0443860	0.7994354	0.0035969
perm	0.7901966	0.4075784	0.8025325	0.4682103
perm	0.0207943	0.5688331	0.6109862	0.0022387
perm	0.0295521	0.4142411	0.0774829	0.0216190
perm	0.0006825	0.2971841	0.1911848	0.0001482
perm	0.0947598	0.5302543	0.2436291	0.0236403
perm	0.0223964	0.0634172	0.5029030	0.0094847
perm	0.0009147	0.0705353	0.1314502	0.0007966
perm	0.0249533	0.1737466	0.2916859	0.0069855
perm	0.1985562	0.8680367	0.6774136	0.0325414
perm	0.0146067	0.0710195	0.1551414	0.0233536
perm	0.0196025	0.9963803	0.2418433	0.0035880
perm	0.6652362	0.3755459	0.8568189	0.3702365
perm	0.8058407	0.5812390	0.8286369	0.3912588
perm	0.0749508	0.8351105	0.0900523	0.0392643
perm	0.0448685	0.4374698	0.1231911	0.0240846
perm	0.0152548	0.0929824	0.3275293	0.0043716
perm	0.7758435	0.4446225	0.9288389	0.4930635
perm	0.0974853	0.0960748	0.9238726	0.0456830
perm	0.0690886	0.1556651	0.0452718	0.2670161
perm	0.0099900	0.0439791	0.5721626	0.0068389
perm	0.5147746	0.6636876	0.3875821	0.2499281
perm	0.0255923	0.0438977	0.4616903	0.0243061
perm	0.0019469	0.0264891	0.3289980	0.0008280
perm	0.1257993	0.0943011	0.2675880	0.0837799
perm	0.0267451	0.1978715	0.8388439	0.0075719
perm	0.1605452	0.4487515	0.3234861	0.0607759
perm	0.0541901	0.3548631	0.4592467	0.0106347
perm	0.0587621	0.8426991	0.0957213	0.0259452
perm	0.0853013	0.1784287	0.6893937	0.0217681
perm	0.0055960	0.0930459	0.7845574	0.0008070
perm	0.0060211	0.1623494	0.1943731	0.0022386
perm	0.0913966	0.0551502	0.3636889	0.0991148
perm	0.3502226	0.3775817	0.5509221	0.1273804
perm	0.1083685	0.0953034	0.8018574	0.0684454
perm	0.8016149	0.4136429	0.7170129	0.7642400
perm	0.0043876	0.0384879	0.1571281	0.0032148
perm	0.0620506	0.6822488	0.5852356	0.0091929
perm	0.0929356	0.4696312	0.0529441	0.1507264
perm	0.0400358	0.1018730	0.5759020	0.0296177
perm	0.0724556	0.0862836	0.3388286	0.0255312
perm	0.4021281	0.4322164	0.7817934	0.1310847
perm	0.0569052	0.0540474	0.5177170	0.0407529
perm	0.0750360	0.8257215	0.2711839	0.0257086
perm	0.0549331	0.7217294	0.2921537	0.0127905
perm	0.1185216	0.2418733	0.2809390	0.0494120
perm	0.4452017	0.9142723	0.6120951	0.1194419
perm	0.0033078	0.1132300	0.5610602	0.0006265
perm	0.9485083	0.6890804	0.8912457	0.6391186
perm	0.7233004	0.9193576	0.5098847	0.3335210
perm	0.0719219	0.1500068	0.1576127	0.0674096
perm	0.6176394	0.1902574	0.8471342	0.9370318
perm	0.2688816	0.1490747	0.7415507	0.1871537
perm	0.0635555	0.7984197	0.8479511	0.0088242
perm	0.0772495	0.1216349	0.2260567	0.0770116
perm	0.6529671	0.2899924	0.8405496	0.4115109
perm	0.1027739	0.3605125	0.6181047	0.0164950
perm	0.0832900	0.0961793	0.2305333	0.0550834
perm	0.0512036	0.1351332	0.5282547	0.0126801
perm	0.0126698	0.0508589	0.9829042	0.0045834
perm	0.0095612	0.2416418	0.5863402	0.0011179
perm	0.1614878	0.2291609	0.6379811	0.0405266
perm	0.0503951	0.7148919	0.1093369	0.0206718
perm	0.1830104	0.2935424	0.9890428	0.0480269
perm	0.1741116	0.5026333	0.2962551	0.0875897
perm	0.0199213	0.0254064	0.5499397	0.0169188
perm	0.2740817	0.4565420	0.5806345	0.0688302
perm	0.0467826	0.1513901	0.2377942	0.0209253
perm	0.1225823	0.3535417	0.6136291	0.0382534
perm	0.5903132	0.1871730	0.7036283	0.7227901
perm	0.4771564	0.1285610	0.7862284	0.6632872
perm	0.0128222	0.0971413	0.6561062	0.0027370
perm	0.0210431	0.0960209	0.2726164	0.0109766
perm	0.2494248	0.6314261	0.1392429	0.1658396
perm	0.2030933	0.2016776	0.3892755	0.0951724
perm	0.5669925	0.9017372	0.4840948	0.2259073
perm	0.0442650	0.3834788	0.6761647	0.0061651
perm	0.0254021	0.1158664	0.1227671	0.0191241
perm	0.0330042	0.4537452	0.0814735	0.0292072
perm	0.0415025	0.0616540	0.3070899	0.0174161
perm	0.0485318	0.3644509	0.9941358	0.0065105
perm	0.2211977	0.2176258	0.2491716	0.1556025
perm	0.0331833	0.5037781	0.6604987	0.0049106
perm	0.1137870	0.4380586	0.3557650	0.0372716
perm	0.7176083	0.8634680	0.5513603	0.3816730
perm	0.1839883	0.2040618	0.5379042	0.0898527
perm	0.0463070	0.6087771	0.4922447	0.0069033
perm	0.1426950	0.1552440	0.1620237	0.1751514
perm	0.0185477	0.0836147	0.4272136	0.0105034
perm	0.5871763	0.8943092	0.5416989	0.1892451
perm	0.0013271	0.3020791	0.8494149	0.0000920
perm	0.0670994	0.0475736	0.9978720	0.0432202
perm	0.0242277	0.0291041	0.5129707	0.0125404
perm	0.0878137	0.1413292	0.6113380	0.0703612
perm	0.3158654	0.3058350	0.6868090	0.1166325
perm	0.3136576	0.2328416	0.9915143	0.1570859
perm	0.0136266	0.0582303	0.3098123	0.0096531
perm	0.5599467	0.3081697	0.8486026	0.3014523
perm	0.0557059	0.0293154	0.4986820	0.0868731
perm	0.1610302	0.1703099	0.4003211	0.0743655
perm	0.1416130	0.6775072	0.0209411	0.8856745
perm	0.3871824	0.1356557	0.4107656	0.9508726
perm	0.0374318	0.0214268	0.2416790	0.1116836
perm	0.0382186	0.0605358	0.8970215	0.0143635
perm	0.0343678	0.3350868	0.1625251	0.0175428
perm	0.0366601	0.0619787	0.4672351	0.0130751
perm	0.5561954	0.3095874	0.7271770	0.3232198
perm	0.1616133	0.1152372	0.8813898	0.0938115
perm	0.1923067	0.3504283	0.5276240	0.0547172
perm	0.7258652	0.5943323	0.3963854	0.4515286
perm	0.0595112	0.4495713	0.5696871	0.0079844
perm	0.1212909	0.4557049	0.8296672	0.0188269
perm	0.0054593	0.1796309	0.7062818	0.0005414
perm	0.6996495	0.6201654	0.3274373	0.5327888
perm	0.0065277	0.0627990	0.9455332	0.0010927
perm	0.1733944	0.1267778	0.1533224	0.3927794
perm	0.2518207	0.2694057	0.1324061	0.6773695
perm	0.1638721	0.0568458	0.9528489	0.1787965
perm	0.3561866	0.2153563	0.5857127	0.1960760
perm	0.1383296	0.1902167	0.8546937	0.0431959
perm	0.0528587	0.7289491	0.0770520	0.0518193
perm	0.0170658	0.3383117	0.0405216	0.0082595
perm	0.1009055	0.0896738	0.7441577	0.0332771
perm	0.0171383	0.0307829	0.3841242	0.0189550
perm	0.3371778	0.0902334	0.5264933	0.5428351
perm	0.1534613	0.6960775	0.5423815	0.0240024
perm	0.4158046	0.4257152	0.4716179	0.1704779
perm	0.1657157	0.1687814	0.8978533	0.0513860
perm	0.1684412	0.0979562	0.8377187	0.1560366
perm	0.4043406	0.8597943	0.0914698	0.9780985
perm	0.0736905	0.0836189	0.6333398	0.0237988
perm	0.6047085	0.3721159	0.9754145	0.3108546
perm	0.1446845	0.1439272	0.7528806	0.0702540
perm	0.0822022	0.2586044	0.6378371	0.0134438
perm	0.0097764	0.3144236	0.3216937	0.0017270
perm	0.2277762	0.3717628	0.2783915	0.0942077
perm	0.0503051	0.1145443	0.1845273	0.0245335
perm	0.2227391	0.0792579	0.2357078	0.6285385
perm	0.1687876	0.1467620	0.8206785	0.0544414
perm	0.4857849	0.3336680	0.3264938	0.3102242
perm	0.0123230	0.1604735	0.2419496	0.0067785
perm	0.0045378	0.1040823	0.0241557	0.0181515
perm	0.3605897	0.1477988	0.5553645	0.2611679
perm	0.0330042	0.4537452	0.0814735	0.0292072
perm	0.5393669	0.1832187	0.5696622	0.7826845
perm	0.0043223	0.0750257	0.2081106	0.0025184
perm	0.0746250	0.0246266	0.1072645	0.8401808
perm	0.0829313	0.5088470	0.2901157	0.0203564
perm	0.3588208	0.2545915	0.6736570	0.2056361
perm	0.0585192	0.0684210	0.4396406	0.0199962
perm	0.0185477	0.0836147	0.4272136	0.0105034
perm	0.5270048	0.4450949	0.7424349	0.2029845
perm	0.0488496	0.2995701	0.4118366	0.0122171
perm	0.0157041	0.8232076	0.5981375	0.0015439
perm	0.0194103	0.7081408	0.0438179	0.0107465
perm	0.3079449	0.2383637	0.9796426	0.1557321
perm	0.1052285	0.0934900	0.8609546	0.0443603
perm	0.0278014	0.0145153	0.7033555	0.0278191
perm	0.1525523	0.4403063	0.4325935	0.0339006
perm	0.0159567	0.1972291	0.4869382	0.0047655
perm	0.0467562	0.0667625	0.1584228	0.0580237
perm	0.0205893	0.0667067	0.8420439	0.0037786
perm	0.2549041	0.1976972	0.6932001	0.1738760
perm	0.6627278	0.8070727	0.8688831	0.2337125
perm	0.0720308	0.5917468	0.8213298	0.0107102
perm	0.0597032	0.0556809	0.7055130	0.0486853
perm	0.0102232	0.0347012	0.9613956	0.0039314
perm	0.1077615	0.5833151	0.7243364	0.0154177
perm	0.0284423	0.1281072	0.6674129	0.0087022
perm	0.0152851	0.1267394	0.1038448	0.0071859
perm	0.1373545	0.4431139	0.4776568	0.0433358
perm	0.0163765	0.0282572	0.2131662	0.0305710
perm	0.1691655	0.6417073	0.2249495	0.0559773
perm	0.0123505	0.0658297	0.1835070	0.0121501
perm	0.6781400	0.8451845	0.6806925	0.2457390
perm	0.8317590	0.8303224	0.3975545	0.7816628
perm	0.7743430	0.5959346	0.5265119	0.5092359
perm	0.1189757	0.1273301	0.5804916	0.0392976
perm	0.5042261	0.2928782	0.7599939	0.2445645
perm	0.0498943	0.0951265	0.0336678	0.2589517
perm	0.0205642	0.3440710	0.3760162	0.0047590
perm	0.3112084	0.1026529	0.6954546	0.2563652
perm	0.0848067	0.9943544	0.2818580	0.0249202
perm	0.1407054	0.3576636	0.4492610	0.0382356
perm	0.0022766	0.4589839	0.4750495	0.0002665

## Warning: Using `bins = 30` by default. Pick better value with the argument
## `bins`.

PPMI500 data curation effort

Brian B. Avants^1,2

Lino Becerra¹

Leon Fonville^2,†

Olivia Hampton^1,†

Alexandra Reardon^1,†

Andrew Stenger^1,†

Xue Wang^1,†

Roger Gunn

Overview

the PPMI500 cohort

1. Data quality is overall high

How do we define M3RI quality?

what is the QC protocol?

what are the QC results?

Tables of Manual QC results

T1w results

Flair results

DT results

rsfMRI results

NM results

do a check based on rsfmri motion – if the qc data “makes sense”

do a check based on DTI – if the qc data “makes sense”

do a check based on NM – if the qc data “makes sense”

do a check based on FLAIR – if the qc data “makes sense”

do a check based on T1w – if the qc data “makes sense”

2. Detailed curation results provide a unique resource in itself

for algorithm developers

for scientists

3. Processing improves quality/usability further

effect of processing by modality

we provide scientists handles on this so that can be “strict” or “loose” in data selection

4. Enables investigation of the “meaning” of M3RI at the individual level

5. Empowers investigation of the “meaning” of M3RI at the population level

simlr

PPMI500 data curation effort

Brian B. Avants1,2

Lino Becerra1

Leon Fonville2,†

Olivia Hampton1,†

Alexandra Reardon1,†

Andrew Stenger1,†

Xue Wang1,†

Roger Gunn

Overview

the PPMI500 cohort

1. Data quality is overall high

How do we define M3RI quality?

what is the QC protocol?

what are the QC results?

Tables of Manual QC results

T1w results

Flair results

DT results

rsfMRI results

NM results

do a check based on rsfmri motion – if the qc data “makes sense”

do a check based on DTI – if the qc data “makes sense”

do a check based on NM – if the qc data “makes sense”

do a check based on FLAIR – if the qc data “makes sense”

do a check based on T1w – if the qc data “makes sense”

2. Detailed curation results provide a unique resource in itself

for algorithm developers

for scientists

3. Processing improves quality/usability further

effect of processing by modality

we provide scientists handles on this so that can be “strict” or “loose” in data selection

4. Enables investigation of the “meaning” of M3RI at the individual level

5. Empowers investigation of the “meaning” of M3RI at the population level

simlr

Brian B. Avants^1,2

Lino Becerra¹

Leon Fonville^2,†

Olivia Hampton^1,†

Alexandra Reardon^1,†

Andrew Stenger^1,†

Xue Wang^1,†